The present invention relates generally to the field of electric power transmission and consumption, and more particularly, to a method and system for generating electric load models.
Electric power generation and consumption includes three broad stages, namely, generation, transmission, and consumption. Generation typically includes one or more power sources that generate electric power. Power sources include non-renewable sources such as fossil fuel based power plants as well as renewable sources such as wind/solar/tidal power plants. The power generated by these power sources is carried to their consumption points through a transmission network that includes transformers, power sub-stations, and power buses. The transmission networks are typically designed to efficiently transfer power from the power sources to consumption points. Consumption may occur through electric machines that may be used at factories or electric appliances that consumers may be using at their homes. Power networks may be designed to cater to different volumes of consumers. For example, some networks may be designed to handle power requirements of a local neighborhood whereas other networks may be designed to handle large cities.
Power system monitoring and control is of utmost importance to provide uninterrupted power supply to consumers. For a detailed view of possible sources of power loss, generation, transmission, and consumption sections of networks are monitored through various mechanisms. Monitoring on these three sections may be carried out either through actual readings obtained from desired sections of the network or through predictive analysis. Multiple systems have been made to predict outputs generated at the generation as well as transmission sections of power networks. Consumption prediction, however, is not always accurate owing to frequent changes in utilization patterns. Consumption prediction may still be manageable with small networks where the utilization patterns may be fairly predictable. However, in small networks, a small change in consumption by a single consumer may lead to a large change in utilization patterns thus making consumption prediction even more difficult. With larger power networks, however, owing to constant new additions of consumers or power consuming appliances, the problem of unpredictable utilization patterns is further complicated.
Many electric load modelling techniques have been devised to predict electric load behavior in power networks. Load modelling techniques may be divided in two major categories—static and dynamic. Current static as well as dynamic load modeling techniques are dependent on data collected during specific events such as blackouts when significant changes occur in input voltage and/or frequency. Existing static load models include algebraic equations that define a relationship between input provided by power sources and an output generated by the electric loads connected to the power sources. These relationships are static and hence do no factor dynamic changes to load conditions. Further, these static relationships also do not change according to varying utilization patterns that are generally observed in power networks.
Dynamic load models, on the other hand, capture time evolution of consumption of power by electric loads. Dynamic load modeling techniques include determining, through a plurality of iterations, a best-fit match of model loads for actual electric loads connected to the power source. However, most of the existing dynamic models are deterministic in nature. In deterministic dynamic load models, a unique load model is determined based on the collected data. Deterministic load models, while being capable of replicating the behavior of the electric network for a particular set of events, may not be useful to understand the effect of unexpected changes to the load consumption patterns and load conditions. This limitation in current models reduces their utility in many practical situations where new electrical loads are added to the network by consumers without prior intimation.
Hence, there is a need for a method and system that provides for a dynamic and probabilistic load model.